This invention relates to the display of tomographic data and more specifically to a method of editing tomographic images in preparation for generating surfaces.
Tomographic data is collected by a wide variety of medical imaging equipment including equipment used for x-ray computed tomography (CT), nuclear magnetic resonance imaging (MR), single photon emission tomography, positron emission tomography, or ultrasound tomography. Tomographic data may be used to construct cross-sectional images of a body, such images being used extensively, for example, in medical diagnosis.
A "projection set" of tomographic data may be reconstructed to produce a single cross sectional image or "slice" image comprised of a matrix of picture elements ("pixels") with each pixel corresponding to a volume element ("voxel") within the imaged body along the slice plane. The pixels of each slice image are stored as digital numbers representing the computed signal intensity at their corresponding voxels. For example, a slice image may be comprised of an array of 512 by 512 pixels each corresponding to one of the 262,144 voxels within the slice of the imaged body.
A typical tomographic imaging study will involve the acquisition of a number of projection sets to produce images of a series of contiguous slices displaced incrementally along an axis. This series of contiguous slice images provides a third spatial dimension of information about the imaged body and increases the amount of pixel data that must be stored and manipulated. A study of 64 contiguous slices may require the generation of over 16 million pixel data words.
A radiologist may visualize the third dimension of the imaged object by viewing the slice images in order of their position along the acquisition axis, or the numerical data of the reconstructed slice images may be viewed by computer programs which produce shaded perspective pictures of the imaged object in three dimensions. This latter approach is preferred for complex three dimensional structures where it is difficult or impossible to understand the three dimensional spatial relationships by reviewing contiguous slices.
Synthesizing a three dimensional image from tomographic data is a two-step process. In the first step, a mathematical description of the surface of the desired object is extracted from the tomographic data. In the second step, a shaded image is synthesized from the mathematical surface description.
Dealing with the second step first, it will be assumed that a surface description comprised of a large number of surface elements ("surfels") may be constructed. The surfels may be operated on by conventional computer graphics techniques, having its genesis in computer-aided design and computer-aided manufacturing, to apply surface shading to objects to aid in image interpretation through a synthesized two-dimensional image. In one such shading method, the surface shading is determined by the distance between the surfel and an imaginary observation point. In a second such method, the surface normal of the surfel, that is, the angle at which the surfel is tipped with respect to an imaginary observation point, determines the surface shading. Generally, the shading is lightest (i.e., most intense) for image elements having surface normals along an operator-selected line of sight and successively darker for those elements inclined to the line of sight. Image elements having surface normals inclined more than 90 degrees from the selected line of sight are hidden in a 3-D object and are suppressed from the display. Foreground objects on the line of sight hide background objects. The shading gives a realistic illusion of three dimensions. In a modified version of the above method, the surface normal is replaced with the gradient of the voxel associated with the surfel.
Returning now to the first step of synthesizing a three dimensional image, producing a mathematical description of the desired surface from the tomographic slice data requires two substeps: 1) the extraction of the object of interest from the rest of the tomographic data, and 2) the fitting of a surface to the extracted object.
The first substep, extraction of the object of interest from the the rest of the tomographic data, is typically performed by differentiating between tissue densities as reflected in the signal intensities associated with each voxel. For example, the signal intensity associated with bone, in an x-ray CT, is substantially different from the signal intensity associated with the surrounding softer tissue and may be used as a surface defining criterion. By establishing a suitable threshold, a simple comparison of the signal intensity of each voxel with the threshold readily distinguishes those voxels associated with the bone rather than the soft tissue.
The above thresholding method works very well when the voxels corresponding to an object-of-interest are the only ones in the tomographic data that fall within the particular thresholding range. This is true of bone in CT and blood vessels in MR, for example. However, many potential objects-of-interest within a body share a density range (or other identifying property) and hence cannot be distinguished by simple thresholding techniques. For example, in CT imaging, images of organs are not readily differentiated. It may be difficult to distinguish even high contrast objects such as bone from other bone-like objects such as the plaster of a cast or other obscuring bones.
In these cases, a method known as connectivity or region growing can be used to separate objects that cannot be distinguished by simple thresholding of signal intensity values. In using connectivity, only voxels connected to a user-identified seed voxel in the object-of-interest will be accepted during the surface extraction step. A voxel is connected to the seed if and only if (1) the voxel is a neighbor (i.e., adjacent to the seed, in a predefined direction) or a neighbor of another connected voxel, and (2) the voxel shares a specified property (e.g., falling within the same threshold range) with the seed voxel. Connectivity has been successfully used in generating three dimensional CT images of soft tissue structures such as the knee ligaments.
The success of such connectivity techniques is dependant on the placement of the seed voxel. A significant problem is "bridges" between the two objects to be separated. A bridge allows the connectivity to spread into the other object. Bridges in the third dimension of the object, where the connection is not visible within a single slice, are particularly hard to detect. Accordingly, for complex structures it may be necessary to try several seed placements before the desired object may be successfully connected. U.S. Pat. No. 4,903,202 issued Feb. 20, 1990 entitled: "Three-Dimensional Object Removal Via Connectivity" and U.S. Pat. No. 4,905,148 issued Feb. 27,1990, both assigned to the same assignee as that of the present invention, entitled "Three-Dimensional Surface Representation Using Connectivity Method Without Leaks", describe the identification and avoidance of such bridges.
In the second substep of producing a mathematical description of the surface of interest, the boundary between the voxels of the object and any non-object voxels must be determined. This determination may be made by using the marching cubes, dividing cubes, or cuberille methods, as are known in the art. The dividing cubes method is described in U.S. Pat. No. 4,719,585, issued to Cline et al. on Jan. 12, 1988, which is incorporated by reference.
In the dividing cubes method, each set of eight cubically adjacent pixels corresponding to voxels in the contiguous slices are examined. The pixels define the vertices of a cube. Each large cube formed in this manner is tested to determine whether the object boundary passes through it. One way to perform this test is to compare the intensity value at each vertex of the cube with the threshold value used to define the object during the extraction step. If some densities are greater and some less than the threshold (or some within the range and some not), then the surface passes through the large cube. In that event the cube is subdivided to form a number of smaller cubes, referred to as subcubes or subvoxels. Densities are calculated by interpolation for the subcube vertices. If the surface passes through a subcube, then the location and the normalized gradient of its vertices, calculated at the center of the subcube, is output to produce a shaded image as described above.
The above techniques of producing a three dimensional image are computationally demanding. Not only is the amount of tomographic data that must be manipulated large, but the steps of extracting the object, generating its surface, and shading the surface require complex repeated operation on the pixel data. For this reason, high speed array processors are often employed for such image generation. Even so, the calculation of a three dimensional image for a 63 slice tomographic series may take on the order of five minutes.
Often the image data is edited by the radiologist prior to three dimensional image generation. Such editing may consist of applying the connectivity methods described above to the image data, to extract a feature of interest, or applying manual editing techniques such as cursor tracing to define or extract a particular region of interest. The time required to reprocess the edited image may add significantly to the time required for three dimensional image generation. If the reprocessing time required by the editing becomes too great, it limits the usefulness of the editing process.
A radiologist may also wish to store several versions of an edited image for future reference, along with an unedited copy of the tomographic data to ensure data integrity. Alternatively, if the memory word size is larger than necessary to store the pixel data, a fixed quantity may be added to the pixels of the unedited image to identify edited bits and yet allow the original data to be reconstructed without storing a separate copy of the original data. In either case considerable data must be stored for each edited image. The storage of several such images places severe demands on the storage medium associated with the imaging device. The time required to load and store such data also adversely affects the speed of the editing process.